wrap forward passes with torch.no_grad() (#19273)

This commit is contained in:
Partho
2022-10-04 19:43:22 +05:30
committed by GitHub
parent d6e920449e
commit a9782881a4

View File

@@ -627,7 +627,8 @@ class BigBirdModelIntegrationTest(unittest.TestCase):
model.to(torch_device)
input_ids = torch.tensor([[20920, 232, 328, 1437] * 1024], dtype=torch.long, device=torch_device)
outputs = model(input_ids)
with torch.no_grad():
outputs = model(input_ids)
prediction_logits = outputs.prediction_logits
seq_relationship_logits = outputs.seq_relationship_logits
@@ -655,7 +656,8 @@ class BigBirdModelIntegrationTest(unittest.TestCase):
model.to(torch_device)
input_ids = torch.tensor([[20920, 232, 328, 1437] * 512], dtype=torch.long, device=torch_device)
outputs = model(input_ids)
with torch.no_grad():
outputs = model(input_ids)
prediction_logits = outputs.prediction_logits
seq_relationship_logits = outputs.seq_relationship_logits
@@ -920,7 +922,8 @@ class BigBirdModelIntegrationTest(unittest.TestCase):
model.eval()
input_ids = torch.tensor([200 * [10] + 40 * [2] + [1]], device=torch_device, dtype=torch.long)
output = model(input_ids).to_tuple()[0]
with torch.no_grad():
output = model(input_ids).to_tuple()[0]
# fmt: off
target = torch.tensor(